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Volume 13
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Correction to Remote Sens. 2020, 12(22), 3803.
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Correction

Correction: Schneider et al. A Satellite-Based Spatio-Temporal Machine Learning Model to Reconstruct Daily PM2.5 Concentrations across Great Britain. Remote Sens. 2020, 12, 3803

by
Rochelle Schneider
1,2,3,*,
Ana M. Vicedo-Cabrera
4,5,
Francesco Sera
1,
Pierre Masselot
1,
Massimo Stafoggia
6,
Kees de Hoogh
7,8,
Itai Kloog
9,
Stefan Reis
10,11,
Massimo Vieno
10 and
Antonio Gasparrini
1,2,12
1
Department of Public Health, Environments and Society, London School of Hygiene & Tropical Medicine, London WC1H 9SH, UK
2
The Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London WC1H 9SH, UK
3
Department of Forecasts, European Centre for Medium-Range Weather Forecast, Reading RG2 9AX, UK
4
Institute of Social and Preventive Medicine, University of Bern, 3012 Bern, Switzerland
5
Oeschger Center for Climate Change Research, University of Bern, 3012 Bern, Switzerland
6
Department of Epidemiology, Lazio Regional Health Service, 00147 Rome, Italy
7
Swiss Tropical and Public Health Institute, Socinstrasse 57, 4051 Basel, Switzerland
8
University of Basel, Petersplatz 1, 4051 Basel, Switzerland
9
Department of Geography and Environmental Development, Ben-Gurion University of the Negev, Beer Sheva P.O. Box 653, Israel
10
UK Centre for Ecology & Hydrology, Bush Estate, Penicuik, Edinburgh, Midlothian EH26 0QB, UK
11
University of Exeter Medical School, Knowledge Spa, Truro TR1 3HD, UK
12
Centre for Statistical Methodology, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK
 
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Remote Sens. 2021, 13(18), 3588; https://doi.org/10.3390/rs13183588
Submission received: 18 June 2021 / Accepted: 14 July 2021 / Published: 9 September 2021
Browse Figure
Versions Notes
In the original article [1], there was a mistake in the legend for Figure 3—The legend contains wider colour ranges and it should be shorter. The correct legend appears below. The authors apologize for any inconvenience caused and state that the scientific conclusions are unaffected. The original article has been updated.

Reference

  1. Schneider, R.; Vicedo-Cabrera, A.M.; Sera, F.; Masselot, P.; Stafoggia, M.; de Hoogh, K.; Kloog, I.; Reis, S.; Vieno, M.; Gasparrini, A. A Satellite-Based Spatio-Temporal Machine Learning Model to Reconstruct Daily PM2.5 Concentrations across Great Britain. Remote Sens. 2020, 12, 3803. [Google Scholar] [CrossRef] [PubMed]
Remotesensing 13 03588 g003 550
Figure 3. Stage-4 predicted PM2.5 concentrations across Great Britain (Top) and London (Bottom) for 2008, 2013, and 2018 aggregated by annual means. All plots were built under the same colour scale.
Figure 3. Stage-4 predicted PM2.5 concentrations across Great Britain (Top) and London (Bottom) for 2008, 2013, and 2018 aggregated by annual means. All plots were built under the same colour scale.
Remotesensing 13 03588 g003
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MDPI and ACS Style

Schneider, R.; Vicedo-Cabrera, A.M.; Sera, F.; Masselot, P.; Stafoggia, M.; de Hoogh, K.; Kloog, I.; Reis, S.; Vieno, M.; Gasparrini, A. Correction: Schneider et al. A Satellite-Based Spatio-Temporal Machine Learning Model to Reconstruct Daily PM2.5 Concentrations across Great Britain. Remote Sens. 2020, 12, 3803. Remote Sens. 2021, 13, 3588. https://doi.org/10.3390/rs13183588

AMA Style

Schneider R, Vicedo-Cabrera AM, Sera F, Masselot P, Stafoggia M, de Hoogh K, Kloog I, Reis S, Vieno M, Gasparrini A. Correction: Schneider et al. A Satellite-Based Spatio-Temporal Machine Learning Model to Reconstruct Daily PM2.5 Concentrations across Great Britain. Remote Sens. 2020, 12, 3803. Remote Sensing. 2021; 13(18):3588. https://doi.org/10.3390/rs13183588

Chicago/Turabian Style

Schneider, Rochelle, Ana M. Vicedo-Cabrera, Francesco Sera, Pierre Masselot, Massimo Stafoggia, Kees de Hoogh, Itai Kloog, Stefan Reis, Massimo Vieno, and Antonio Gasparrini. 2021. "Correction: Schneider et al. A Satellite-Based Spatio-Temporal Machine Learning Model to Reconstruct Daily PM2.5 Concentrations across Great Britain. Remote Sens. 2020, 12, 3803" Remote Sensing 13, no. 18: 3588. https://doi.org/10.3390/rs13183588

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